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James Taylor

I will use this blog to discuss business challenges and how technologies like analytics, optimization and business rules can meet those challenges.

About the author >

James is the CEO of Decision Management Solutions and works with clients to automate and improve the decisions underpinning their business. James is the leading expert in decision management and a passionate advocate of decisioning technologies business rules, predictive analytics and data mining. James helps companies develop smarter and more agile processes and systems and has more than 20 years of experience developing software and solutions for clients. He has led decision management efforts for leading companies in insurance, banking, health management and telecommunications. James is a regular keynote speaker and trainer and he wrote Smart (Enough) Systems (Prentice Hall, 2007) with Neil Raden. James is a faculty member of the International Institute for Analytics.

Recently in Business Intelligence Category

A friend sent me a link to a webinar on "The End of BI as We Know It" that promised "A fresh look at what business analytics means". It wasn't clear who was speaking or what company was sponsoring but the title intrigued me (as it was meant to). But when I looked at the body of the description I was underwhelmed. There was, frankly, nothing new. The webinar promised to explain several things - presumably things that were "fresh" or not "BI as we know it". But here's the list (edited to summarize):

  • Speed to deploy, to build, to get analysts serving themselves is critical
  • Must be able to analyze data from production databases and handle millions or billions of records
  • It's critical to combine multiple data sources from the data warehouse to Excel
  • Must be able to easily and quickly build dashboards

All this is pretty mainstream as far as I am concerned - no-one wants a BI tool that is slow, that can't access data from various sources, that can't handle lots of rows or that doesn't let you build dashboards.

And there was nothing about decision making, nothing about supporting different kinds of decision-making (from collaborative, strategic decisions to high-volume operational decisions), nothing about data mining or predictive analytics, nothing that fundamentally changes how companies can put data to work improving their business.

I gave a speech some months back in South Africa called "Does BI Matter" (audio and slides here in a large PDF) and I have blogged before about Why thinking about decisions should be a BI best practice. If you think you can improve day-to-day operations by giving everyone dashboard or reports then you haven't visited your call center lately. If you think that the way your systems work, the way your website works, should not also be improved by applying analytics then you underestimate the extent to which your systems are your business. If you think the time it takes to build reports or the ease with which you can build dashboards are the critical measures of success then you are focused on means and not ends.

The reason you spend money on Business Intelligence is to provide intelligence about your business not just so you can have a BI platform. You have BI to make better decisions, to improve the way you run your business. If you don't know which decisions you are improving then you are unlikely to make progress.

If that list really counts as "fresh" and "the end of BI as we know it" then we should all be worried....

Posted October 7, 2010 9:48 AM
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I was struck today by a short but effective Information Builders PowerPoint - Four Worst and Four Best Practices in Business Intelligence. I really liked the worst practices - especially the one about assuming that business people have the skills or time to learn to use a BI tool. I blogged not long ago about the problem that most people are not very good at math and this is just as true when considering BI more generally as when thinking about data mining and analytics.

It's also true that many of the people targeted by BI tools don't have the time to use drill-down and analysis tools. Think about the folks in the call center - they want answers, not an ability to explore, so that they can finish the call. This is why it is important to think about the decisions involved and who you want making them. Knowing the decision and the decision maker will help you determine if you need BI tools to help decide or analytics and rules to automate that decision. And remember, just because someone passes on the result of a decision does not mean that the same person is qualified to make the decision. A call center representative might be the one to pass on a denial of a refund for instance but you might want someone else to decide which refunds get denied. Automating the decision can allow one person to control how the decision is made while others pass on these decisions.

I was also struck by the worst practice of selecting a BI tool without a specific business need. I spoke about this when I presented in South Africa earlier this year. If the reason you buy a BI tool is just to have BI then you probably aren't helping your company as much as you could be. Understand the business drivers - the decisions that must be made, the reports required for compliance - and you will do better. You can check out the slides and audio from this presentation on my website - Does BI Matter? (large file warning)

And this brings us back to my favorite Best practice - identify your business need upfront. Or, I would say, begin with the decision in mind.

Posted September 9, 2010 6:30 PM
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Posted August 30, 2010 11:59 AM
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There was a great article this week over on the Requirements Network - Data Warehouse / Business Intelligence Requirements Elicitation. Where do You Begin? I really liked the fact that early in the discussion the author said:
After establishing these strategic objectives, make it a priority to get your users talking about their work day, struggles, obstacles, and how they make business decisions as pertains to data (my emphasis).
I think it is essential when working with data to focus on decisions and on how data and analytics might improve those decisions. I also liked the focus on data mining as one of the steps - not just reporting and "soft" analysis tools - though I would add that deploying the results of data mining needs some serious consideration also. In this vein I also wrote about data integration and the importance of keeping the decision in mind.

Posted August 24, 2010 9:46 AM
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Some time ago I was at a warranty conference and there was an interesting discussion about registration cards. You know, those postcard sized mailers you are asked to return to register your product. They often have all sorts of demographic and interest questions - asked by the company to flesh out its 360-degree view of its customers.One of the speakers was asked about this and he argued that, in fact, companies should ask for the absolute minimum information on these cards. This would, he said, increase response rates and would have little or no effect on the value of the data because all the demographic data could be purchased anyway once you had the list of customers and some basic information about them. In other words companies were identifying fewer customers because they were worrying too much about the amount of information they have about those customers. I took a couple of lessons away from this.

First, always consider the potential for external data to improve an internal process. Just because you want some data it does not mean you have to ask the customer for it. Buying external data and integrating it might be more cost-effective. And you might find you can infer the data analytically too, using historical records like purchases or returns to derive customer characteristics like preferences or approach to online purchasing.

Second it reminded me of the importance of beginning with the decision in mind. Too often I see companies embarking on data integration and quality initiatives designed to improve all their data - presumably so they can make better decisions - without really thinking through what those decisions are. If you begin, instead, with the decision, then you might find that you only need some of your data integrated, that some of it is good enough to make the decision (even though it is pretty dirty) or that some of the data you need has to be sourced from outside the company anyway.  If you don't know which decision you wish to make or improve then you can't know which data is truly important.

Posted July 28, 2010 12:55 PM
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